where \(S\)  is the set of all instance-label pairs, \(\epsilon _y \propto 1 /{\sqrt{\mathbb {P}( y )}}\) ,
and \(\mu _y\)  is the centroid in the embedding space of all samples in class \(y\) .
[1]} proposed a similar loss with \(\epsilon _y = 0\) .
Like our ELM method, DRO-LT explicitly seeks to improve the quality of learned embeddings for tail classes.
However, there are important distinctions:
